Vaccine‐Preventable Travel Health Risks: What Is the Evidence—What Are the Gaps?
Bibliographic record
Abstract
BACKGROUND: Existing travel health guidelines are based on a variety of data with underpinning evidence ranging from high-quality randomized controlled trials to best estimates from expert opinion. For strategic guidance and to set overall priorities, data about average risk are useful. The World Health Organization (WHO) plans to base future editions of "International Travel and Health" on its new "Handbook for Guideline Development." METHODS: Based on a systematic search in PubMed, the existing evidence and quality of data on vaccine-preventable disease (VPD) risks in travelers was examined and essentials of vaccine efficacy were briefly reviewed. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework was used to evaluate the quality of the data. RESULTS: Moderate-quality data to determine the risk of VPD exist on those that are frequently imported, whereas in most others the level of confidence with existing data is low or very low. CONCLUSIONS: In order for the WHO to produce graded risk statements in the updated version of "International Travel and Health," major investment of time plus additional high-quality, generalizable risk data are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".